India’s Demographics and the Total Fertility Rate

For many, many years, this was my slide on India’s TFR in lectures I used to give on India’s demographics:

Wikipedia (Old data)

What is TFR? Here’s Wikipedia:

“The total fertility rate (TFR) of a population is the average number of children that would be born to a woman over her lifetime if:

  1. she were to experience the exact current age-specific fertility rates (ASFRs) through her lifetime
  2. she were to live from birth until the end of her reproductive life.”

Hans Rosling had a better, more intuitive term: babies per women. Here’s an excellent chart from Gapminder, although ever so slightly outdated:

Click here to see the original chart, and please press on the play button to see this change over time

Here’s the excellent Our World In Data page about the topic, and here’s a lovely visualization of how the TFR has changed for the world and for India over time (please make sure to “play” the animation):

(I hope this renders on your screens the way it is supposed to. If not, my apologies, and please click here instead)

But now we have news: India’s TFR has now slipped below the replacement rate. Here’s Vivek Kaul in Livemint explaining what this means:

The recently released National Family Health Survey (NFHS-5) of 2019-2021 shows why. As per the survey, India’s total fertility rate now stands at 2. It was 3.2 at the turn of the century and 2.2 in 2015-2016, when the last such survey was done. This means that, on average, 100 women had 320 children during their child-bearing years (aged 15-49). It fell to 220 and now stands at 200.
Hence, India’s fertility rate is already lower than the replacement level of 2.1. If, on average, 100 women have 210 children during their childbearing years and this continues over the decades, the population of a country eventually stabilizes. The additional fraction of 0.1 essentially accounts for females who die before reaching child-bearing age.

https://www.livemint.com/opinion/columns/the-women-who-went-missing-in-our-demographic-dividend-11652200177580.html

And here’s the breakup by state, updated for the latest results:

By iashris.com – https://indiainpixels.xyz, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=112844699

Of course, as with all averages, so also with this one: you can weave many different stories based on how you slice the data. You can slice it by urban/rural divides, you can slice it by states, you can slice it by level of education, you can slice it by religion – and each of these throws up a different point of view and a different story.

But there are three important things (to me) that are worth noting:

  1. The TFR for India has not just come down over time, but has slipped below the global TFR in recent years.
  2. This doesn’t (yet) mean that India’s population will start to come down right away, and that for a variety of reasons. As Vivek Kaul puts it:
    “So, what does this mean? Will the Indian population start stabilizing immediately? The answer is no. This is primarily because the number of women who will keep entering child-bearing age will grow for a while, simply because of higher fertility rates in the past. Also, with access to better medical facilities, people will live longer. Hence, India’s population will start stabilizing in around three decades.”
  3. The next three to four decades is a period of “never again” high growth opportunity for India, because never again (in all probability) will we ever have a young, growing population.

Demography is a subject you need to be more familiar with, and if you haven’t already, please begin with Our World in Data’s page on the topic, and especially spend time over the section titled “What explains the change in the number of children women have?”

A Sunny Outlook

Some years ago, I wrote a chapter in a book called Farming Futures. The book is about social entrepreneurship in India, and my chapter was about a firm called Skymet. Skymet is a private weather forecasting firm based partially out of Pune and partially out of Noida (along with other office in other locations). But researching for the chapter got me interested in both how the art and science of weather forecasting had developed over time, and where it is headed next.

Only trivia enthusiasts are likely to remember the name of the captain on whose ship Charles Darwin made his historic voyage that was to result in the publication of “On the Origin of Species”. Fewer still will remember that Admiral Robert FitzRoy committed suicide. The true tragedy, however, is that it is almost certainly his lifelong dedication to predicting the weather that caused him to take his own life.
We have, in the decades and centuries since, come a long way. Weather forecasting today is far more advanced than it was in Admiral FitzRoy’s day. Britain, for example, Admiral FitzRoy’s own nation, today has an annual budget of more than 80 million GBP to run its meteorological department. It has an accuracy of around 95% when it comes to forecasting temperatures, and an accuracy of around 75% when it comes to forecasting rain – anybody who is even remotely familiar with Britain’s notoriously fickle weather would know that this is no small achievement.

Farming Futures: Emerging Social Enterprises in India

Those numbers that I cited, and the tragic story of Admiral FitzRoy, come from a lovely book called The Weather Experiment.


But I first read about weather, and the difficulties associated with forecasting it in a book called Chaos, by James Gleick:

Lorenz enjoyed weather—by no means a prerequisite for a research meteorologist. He savored its changeability. He appreciated the patterns that come and go in the atmosphere, families of eddies and cyclones, always obeying mathematical rules, yet never repeating themselves. When he looked at clouds, he thought he saw a kind of structure in them. Once he had feared that studying the science of weather would be like prying a jack-in–the-box apart with a screwdriver. Now he wondered whether science would
be able to penetrate the magic at all. Weather had a flavor that could not be expressed by talking about averages. The daily high temperature in Cambridge, Massachusetts, averages 75 degrees in June. The number of rainy days in Riyadh, Saudi Arabia, averages ten a year. Those were statistics. The essence was the way patterns in the atmosphere changed over time…

Ch. 1, The Butterfly Effect, Chaos, by James Gleick

What is the Butterfly Effect, you ask? It gets its own Wikipedia article, have fun reading it.


All of which is a very long way to get around to the write-up we’re going to be talking about today, called After The Storm.

On 29 October 1999, a “Super Cyclone” called Paradip devastated parts of Odisha and the east coast of India. At wind speeds of almost 250 kms per hour, it ravaged through the land, clearing out everything in its path. Fields were left barren, trees uprooted like mere matchsticks, entire towns devastated. More than 10,000 people lost their lives.
Fast forward to two decades later. In 2020, bang in the middle of the Covid-19 pandemic, another cyclone—known as Amphan—speeds through the Bay of Bengal. It crashes into the land like Paradip did in 1999. Like before, many homes are destroyed and structures uprooted. But one thing is different: this time’s death toll is 98. That’s a 100 times lower than 1999’s casualties.
What made this difference possible? Simply put: better, timely and more accurate weather prediction.

https://fiftytwo.in/paradigm-shift/after-the-storm/

We’ve made remarkable progress since the days of Admiral FitzRoy. Predicting the weather is still, admittedly, a very difficult and very expensive thing, as this lovely little write-up makes clear, but it is also something we’re much better at these days. We have better instruments, better computing power, better mathematical and statistical tools to deploy, and the ability to synthesize all of these to come up with much better forecasts – but it’s not perfect, and it’s not, well, good enough.

Those last two words aren’t meant as a criticism or a slight – far from it. The meteorologists themselves feel that is is not good enough:

“It almost becomes like flipping a coin,” Professor Islam says. “The IMD is not to be blamed. They will be very good at predicting the weather three or four days in advance. Beyond that, it cannot be done because there is a fundamental mathematical limitation to these questions.”
“IMD can do another sensor, another satellite, they can maybe improve predictions from two days, to three days. But can they do ten days? There is no evidence. Right now there is no weather forecasting model on the globe. India to Europe to Australia, it doesn’t matter, it’s not there.”

https://fiftytwo.in/paradigm-shift/after-the-storm/

As Professor Islam says, he wants to move from up from being able to forecast the next four to five days, to being able to predict weather over the next ten days. Why? So that communities in the path of a storm have adequate time to move. What could be more important than that when it comes to meteorology.


So what’s the constraint? This is a lovely analogy:

“I give this example to my students,” the professor says, “Look, usually all of science and AI is based on this idea of driving with the rearview mirror. I don’t have an option, so I’m looking into my rearview mirror and driving. I will be fine as long as the road in the front exactly mirrors the rearview. If it doesn’t and I go into a turn? Disastrous accident.”

https://fiftytwo.in/paradigm-shift/after-the-storm/

It’s weird what the human brain will choose to remind you of, but this reminds me, of all things, of a gorilla. That too, a gorilla from a science fiction book:

Amy distinguished past, present, and future—she remembered previous events, and anticipated future promises—but the Project Amy staff had never succeeded in teaching her exact differentiations. She did not, for example, distinguish yesterday from the day before. Whether this reflected a failing in teaching methods or an innate feature of Amy’s conceptual world was an open question. (There was evidence for a conceptual difference.) Amy was particularly perplexed by spatial metaphors for time, such as “that’s behind us” or “that’s coming up.” Her trainers conceived of the past as behind them and the future ahead. But Amy’s behavior seemed to indicate that she conceived of the past as in front of her—because she could see it—and the future behind her— because it was still invisible.

Michael Crichton, Congo

That makes a lot of sense, doesn’t it? And that’s the fundamental problem with any forecasting tool: it necessarily has to be based on what happened in the past, because what else have we got to work with?

And if, as Professor Islam says, the road in the future isn’t exactly like the past, disaster lies ahead.


But Artificial Intelligence and Machine Learning need not be about predicting what forms the storms of the future might take. They can be of help in other ways too!

“It hit us that the damage that happened to the buildings in the poorer communities could have been anticipated very precisely at each building’s level,” Sharma explains. “We could have told in advance which roofs would fly away, and which walls would collapse, which not so. So that’s something we’ve tried to bring into the AI model, so that it can be a predictive model.”

“What we do is, essentially, this: we use satellite imagery or drone imagery and through that, we identify buildings. We identify the material and technology of the building through their roofs as a proxy, and then we simulate a sort of a risk assessment of that particular building, right? We also take the neighbouring context into account. Water bodies, how high or low the land is, what kind of trees are around it, what other buildings are around it.”

The team at SEEDS and many others like it are more concerned about the micro-impact that weather events will have. Sharma is interested in the specifics of how long a building made from a certain material will be able to withstand the force of a cyclone. This is an advanced level of interpretation we’re talking about. It’s creative, important and life-saving as well.

https://fiftytwo.in/paradigm-shift/after-the-storm/

In other words, we may not know the intensity of a particular storm, and exactly when and where it will hit. But given assumptions of the intensity of a storm, can we predict which buildings will be able to withstand a given storm and which ones won’t?

This is, as a friend of mine to whom I forwarded this little snippet said, is very cool.

I agree. Very cool indeed.

And sure, accuracy about weather forecasting may still be a ways away, and may perhaps lie forever beyond our abilities. But science, mathematics and statistics might still be able to help us in other ways, and that (to me) still counts as progress.

And that is why, all things considered, I’d say that when it comes to the future of weather forecasting, sunny days are ahead.


In case you haven’t already, please do subscribe to fiftytwo.in

Excellent, excellent stories, and the one I have covered today is also available in podcast form, narrated by Harsha Bhogle, no less. All their other stories are wroth reading too, and I hope you have as much fun going through them as I have.

Lessons from the eradication of smallpox

Vox has a nice and short read out on the battle against smallpox, and lessons we might learn today from how and where the battle was waged, at what costs, and with what effects.

But for all that the world has lost in the last few years, the history of infectious disease has a grim message: It could have been even worse. That appalling death toll resulted even though the coronavirus kills only about 0.7 percent of the people it infects. Imagine instead that it killed 30 percent — and that it would take centuries, instead of months, to develop a vaccine against it. And imagine that instead of being deadliest in the elderly, it was deadliest for young children.
That’s smallpox.

https://www.vox.com/future-perfect/21493812/smallpox-eradication-vaccines-infectious-disease-covid-19

My notes after having read the article:

  1. Smallpox is estimated to have killed between 300 million to 500 million people in the 20th century alone
  2. We still do not have an effective treatment against smallpox
  3. There are two different viruses that cause smallpox: variola major and variola minor
  4. We no longer need to explain R0 to anybody, thanks to covid, but this point is staggering: it had an infectiousness of between 5 to 7, and a mortality rate of 30%.
  5. “In China, as early as the 15th century, healthy people deliberately breathed smallpox scabs through their noses and contracted a milder version of the disease. Between 0.5 percent and 2 percent died from such self-inoculation, but this represented a significant improvement on the 30 percent mortality rate of the disease itself.”
    What a horrible lottery to play. Would you play this lottery? This, by the way, is one of the many reasons why learning statistics and probability is worth your time.
  6. Learn more about Edward Jenner.
  7. We have better ways of shipping vaccines across the world these days, but what a story this is!
    “Spain especially struggled to reach its colonies in Central and South America, so in 1803, health officials in the country devised a radical new method for distributing the vaccine abroad: orphan boys.
    The plan involved putting two dozen Spanish orphans on a ship. Right before they left for the colonies, a doctor would give two of them cowpox. After nine or 10 days at sea, the sores on their arms would be nice and ripe. A team of doctors onboard would lance the sores, and scratch the fluid into the arms of two more boys. Nine or 10 days later, once those boys developed sores, a third pair would receive fluid, and so on. (The boys were infected in pairs as backup, just in case one’s sore broke too soon.) Overall, with good management and a bit of luck, the ship would arrive in the Americas when the last pair of orphans still had sores to lance. The doctors could then hop off the ship and start vaccinating people.”
  8. Institutions matter:
    “It was not until the 1950s that a truly global eradication effort began to appear within reach, thanks to new postwar international institutions. The World Health Organization (WHO), founded in 1948, led the charge and provided a framework for countries that were not always on friendly terms to collaborate on global health efforts.”
  9. Culture matters:
    “Efforts by the British Empire to conduct a smallpox vaccination program in India made less progress, due in large part to mistrust by the locals of the colonial government.”
  10. Science matters:
    ” “There was no shortage of people telling [the people involved in the eradication effort] that their effort was futile and they were hurting their career chances,” former CDC director William Foege wrote in his 2011 book House on Fire about the smallpox eradication effort.
    But other advances had brought it within reach. Needle technology had improved, with new bifurcated needles making it possible to use less vaccine. Overseas travel improved, which made it easier to ship vaccines and get public health workers where they were most needed, and provided impetus for worldwide eradication as it made it more likely that a smallpox outbreak anywhere in the world could spread.”

As always, read the whole article. I’ll quote here the concluding paragraph from the piece, and I’d urge you to reflect on it:

The devastation of Covid-19 has hopefully made us aware of the work public health experts and epidemiologists do, the crucial role of worldwide coordination and disease surveillance programs (which are still underfunded), and the horrors that diseases can wreak when we can’t control them.
We have to do better. The history of the fight against smallpox proves that we’re capable of it.

https://www.vox.com/future-perfect/21493812/smallpox-eradication-vaccines-infectious-disease-covid-19

The IPL and the Benefits of Competition

I was gloriously and completely wrong about the IPL, and I couldn’t be happier about being wrong. So happy, in fact, that I can’t stop talking about how wrong I was (see here, here and here). It has been nothing but beneficial for Indian cricket, and I would argue this holds true for world cricket at large.

It’s one thing to say this in 2022 with the benefit of hindsight, and it is quite another to have said it in March 2008! Here’s Amit Varma from what seems like ages ago:

The problem with cricket in most cricket-playing countries, certainly in India, is that the cricket market is what economists call a monopsony. A monopsony is a market in which there is only one buyer for a particular class of goods and services. Until now, a young Indian cricketer who wanted to play at the highest level could only sell his services to the BCCI. If it treated him badly and did not give him his due rewards, he had no other options open to him.

https://www.espncricinfo.com/story/opportunity-choice-and-the-ipl-342143

I’ve quoted from this piece before, and I would strongly urge you to go read it again. I always do this, of course, but the reason I’m doing so in this particular case is because it is always a pleasure to read a piece that uses economic theory to make predictions that turn out to be spot on.

Here’s Ian Chappell in a more recent piece:

Apart from the massive financial boost and enormous increase in fan interest, India’s biggest gain from a highly productive IPL competition has been the huge improvement in playing depth.
About 20 years ago, India’s overseas reputation was an improving one, especially under the captaincy reign of a competitive Sourav Ganguly but the pace of that ascent gradually increased when the IPL began 15 seasons back, in 2008. The quietly thoughtful MS Dhoni – who is still exerting an influence – built on Ganguly’s reputation, which was then improved upon by the highly competitive leadership of Virat Kohli.
The firmly established IPL is now seen as the most important part of India’s enviable depth in international cricket.

https://www.espncricinfo.com/story/ian-chappell-india-have-the-ipl-to-thank-for-their-formidable-international-depth-1313912

But what are the economic factors that have been at play in making the IPL such A Good Thing for cricket in general, and Indian cricket in particular?

Amit listed out the following factors:

  1. The BCCI stopped being a monopsony. Ten(as of this year) franchises bidding for a player, with a reasonably well established feeder system is a very different proposition to depending upon the whims and fancies of a deeply flawed selection system, and the results are there for all to see.
  2. The IPL is a competition that is about the money, and is about the bottom-line, and this is a good thing. Something that I should have known, but was too besotted with my love of test cricket to see. It forces players to be selected on merit, and also dropped on merit, and merit alone.
  3. The ecosystem for spotting, nurturing and promoting talent is only likely to get better over time was his prediction, and see this article about Kumar Kartikeya Singh, and this article about Tilak Varma, published this year on ESPNCricinfo. And if you’re hungry for more, see this on T N Natarajan, and this on Washington Sundar. Sports fans will see the struggle in these stories, but if you think about it from the point of view of an economist, you should credit the IPL for creating the ecosystem that enables the emergence of these players. And indeed, many more to come.

Ian Chappell is making the same points in his write-up as Amit Varma, but for Amit to have done this in 2008, and by using simple economic theory is remarkable. We would do well to absorb the lesson that I think can be learnt from this: don’t be blinded by distractions, and trust in economic theory to work well more often than not.

This is how Amit concluded his piece back then:

Having said that, the IPL could fail, for not every good idea is rewarded with smart execution. Maybe the franchises got carried away and bid too high (game theorists call it “the winner’s curse”). Maybe the games will not get high enough TRPs, as a cricket-loving public deluged with an overdose of cricket finds other ways to entertain itself. If it does flounder, it will be a pity, for its failure will be remembered and used to prevent other such experiments.
On the other hand, if the IPL succeeds, cricket historians may one day write about 2008 as the year that cricket discovered its future.

https://www.espncricinfo.com/story/opportunity-choice-and-the-ipl-342143

It is safe to say that it is the second paragraph that is applicable today, not the first.

And it wouldn’t be the worst idea to learn some of the principles of economics by studying the IPL!

Have we become uniquely stupid?

For those of you who have read the essay, the title of today’s blogpost is a dead giveaway: I am referring to Jonathan Haidt’s essay in the Atlantic, titled “Why The Past 10 Years Of American Life Have Been Uniquely Stupid“. The subtitle is equally depressing: It’s Not Just a Phase.

It’s been clear for quite a while now that red America and blue America are becoming like two different countries claiming the same territory, with two different versions of the Constitution, economics, and American history. But Babel is not a story about tribalism; it’s a story about the fragmentation of everything. It’s about the shattering of all that had seemed solid, the scattering of people who had been a community. It’s a metaphor for what is happening not only between red and blue, but within the left and within the right, as well as within universities, companies, professional associations, museums, and even families.
Babel is a metaphor for what some forms of social media have done to nearly all of the groups and institutions most important to the country’s future—and to us as a people. How did this happen? And what does it portend for American life?

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

The essay is a lengthy read, but a rewarding one. Jonathan Haidt takes us through the evolution of the internet, with the emphasis on the social aspect really beginning to take off post 2010 or so, and gives us a book to read that goes on my to-read list: Nonzero: History, Evolution and Human Cooperation.

The next section is where the story really picks up, for we are introduced to the “villains” of the piece: the Like, Share and Retweet buttons. It’s not the buttons themselves that are to blame, of course, much like the atom not being at fault for the atom bomb. It’s what we have done with the Like, Share and Retweet buttons that is the problem:

By 2013, social media had become a new game, with dynamics unlike those in 2008. If you were skillful or lucky, you might create a post that would “go viral” and make you “internet famous” for a few days. If you blundered, you could find yourself buried in hateful comments. Your posts rode to fame or ignominy based on the clicks of thousands of strangers, and you in turn contributed thousands of clicks to the game.

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

Goodhart’s Law is massively underrated. Rather than optimizing for the quality of the content of one’s creation, we optimize for it’s virality. The virality ought to be a function of the quality, but we’ve skipped the intermediate step, with consequences that have become manifest and deep-rooted. Or as Jonathan Haidt puts it, “these platforms were almost perfectly designed to bring out our most moralistic and least reflective selves”.

He then goes on to quote from Madison’s Federalist No. 10 on the innate human proclivity towards “faction”.
I have watched “The Last Dance” on Netflix more times than I should have, but this reminds me of Michael Wilbon talking about how everybody in Chicago hated the Pistons (around the 28 minute mark in episode 4, if you’re interested). He repeatedly involves the phrase “this was personal”, and that’s one way to understand what factionalism means. Tribalism in sports, but elsewhere too, is the kind of factionalism you want to think about in this context, and you might also benefit from reading the transcript of Ezra Klein’s conversation with Tyler Cowen:

https://conversationswithtyler.com/episodes/ezra-klein-2/

Factionalism (or tribalism. I’m not sure if the two mean exactly the same thing in an academic sense, but I am using them interchangeably here) hasn’t necessarily gone down, but we seem to have found new things to be “tribal” about.

As I understand it, Haidt is making the point that our tribalism when it comes to politics is now more deep-rooted than ever, but is also more trivial than ever before. Which politician is wearing what kind of clothes for which occasion excites more debate online than substantive issues that warrant more debate. Or as I prefer to put it, our agreement with stated positions and policies is these days a function of who said it, rather than what has been said. Such tribal loyalty when it comes to close friends is one thing, although even that has its limits, but fealty of such an extreme nature when it comes to political discourse ought to worry most of us.

And as an aside, the last question that Tyler Cowen asks in that extract above is a question to which I don’t have a great answer. I agree with the point in his question, but like him, wonder about the underlying cause.


An extract twice removed now:

The digital revolution has shattered that mirror, and now the public inhabits those broken pieces of glass. So the public isn’t one thing; it’s highly fragmented, and it’s basically mutually hostile. It’s mostly people yelling at each other and living in bubbles of one sort or another.

https://www.vox.com/policy-and-politics/2019/12/26/21004797/2010s-review-a-decade-of-revolt-martin-gurri

Amit Varma made a very similar point in a recent podcast with Shruti Kapila recently, in which he pointed out that social media has, in effect, decentralized the news (I’m quoting from memory here, so please forgive me if I’ve got the exact wording wrong). Amit Varma says that this is on balance a good thing, but with some negative consequences. Jonathan Haidt disagrees:

Mark Zuckerberg may not have wished for any of that. But by rewiring everything in a headlong rush for growth—with a naïve conception of human psychology, little understanding of the intricacy of institutions, and no concern for external costs imposed on society—Facebook, Twitter, YouTube, and a few other large platforms unwittingly dissolved the mortar of trust, belief in institutions, and shared stories that had held a large and diverse secular democracy together.

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

Where do I fall on this Haidt-Verma spectrum? Closer towards the Haidt end, I’d say, but I do have to remind myself that I have written this and you are reading it, so maybe decentralization isn’t all that bad? But that’s as far as I’m willing to go – on balance, I find myself closer to Haidt’s position, at least for the moment.


But the enhanced virality of social media thereafter made it more hazardous to be seen fraternizing with the enemy or even failing to attack the enemy with sufficient vigor. On the right, the term RINO (Republican in Name Only) was superseded in 2015 by the more contemptuous term cuckservative, popularized on Twitter by Trump supporters. On the left, social media launched callout culture in the years after 2012, with transformative effects on university life and later on politics and culture throughout the English-speaking world.

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

Haidt is writing this from an American perspective, for an American audience. But we in India have our own share of names for The Other, don’t we? It’s not just the fact that we have relatively trivial tribalism in areas as important as political discourse, but the fact that the discourse itself is not just trivial, but downright nasty. And the nastier it gets, the higher the support from your own side!


I’ll skip talking about a couple of sections from Haidt’s essay, not because they’re not important, but because they aren’t directly relevant to us here in India. But the subtitle of his essay gets an entire section, where he speaks about how things are likely to get much worse in the years (months) to come:

in a 2018 interview, Steve Bannon, the former adviser to Donald Trump, said that the way to deal with the media is “to flood the zone with shit.” He was describing the “firehose of falsehood” tactic pioneered by Russian disinformation programs to keep Americans confused, disoriented, and angry. But back then, in 2018, there was an upper limit to the amount of shit available, because all of it had to be created by a person (other than some low-quality stuff produced by bots).
Now, however, artificial intelligence is close to enabling the limitless spread of highly believable disinformation. The AI program GPT-3 is already so good that you can give it a topic and a tone and it will spit out as many essays as you like, typically with perfect grammar and a surprising level of coherence. In a year or two, when the program is upgraded to GPT-4, it will become far more capable. In a 2020 essay titled “The Supply of Disinformation Will Soon Be Infinite,” Renée DiResta, the research manager at the Stanford Internet Observatory, explained that spreading falsehoods—whether through text, images, or deep-fake videos—will quickly become inconceivably easy. (She co-wrote the essay with GPT-3.)

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

Speaking of the amount of shit that had to be created by a person, read this article written by Samanth Subramanian in February 2017.


So what might be done? Jonathan Haidt has a three-pronged solution:

What changes are needed? Redesigning democracy for the digital age is far beyond my abilities, but I can suggest three categories of reforms––three goals that must be achieved if democracy is to remain viable in the post-Babel era. We must harden democratic institutions so that they can withstand chronic anger and mistrust, reform social media so that it becomes less socially corrosive, and better prepare the next generation for democratic citizenship in this new age.

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

He outlines the steps involved in each of these, and if you haven’t already, I would encourage you to go read the entire essay, and these outlines in particular. I find myself to be in broad agreement with both the suggestions as well as how they might be implemented, but also worry about whether we have the political and social will to actually do so.


Finally, a coda of sorts:

The most pervasive obstacle to good thinking is confirmation bias, which refers to the human tendency to search only for evidence that confirms our preferred beliefs. Even before the advent of social media, search engines were supercharging confirmation bias, making it far easier for people to find evidence for absurd beliefs and conspiracy theories, such as that the Earth is flat and that the U.S. government staged the 9/11 attacks. But social media made things much worse.

https://www.theatlantic.com/magazine/archive/2022/05/social-media-democracy-trust-babel/629369/

And I would feel very bad if you, the reader, were to read either my post or Haidt’s essay in order to confirm your already existing fears about the ill-effects of social media. And so I urge you to read this column by Tyler Cowen next:

Calling something “extremist” is not an effective critique. It’s a sign that the speaker or writer either doesn’t want to take the trouble to make a real argument, or is hoping to win the debate through rhetoric or Twitter pressure rather than logic. It’s also a bad sign when critics stress how social media have fed and encouraged “extremism.”


What the U.S. needs is more consideration of more extreme ideas. If you see someone inveighing against “extremism” or “extremist ideas,” beware: That is itself an extreme position. True moderation lies in calm and reasoned debate.

https://www.bloomberg.com/opinion/articles/2022-05-06/extremist-ideas-are-not-always-bad-and-are-often-popular

My take on this essay? I think Tyler is saying that we shouldn’t be throwing the baby out with the bathwater. Social media has done two things: made it easier to spread “extreme” ideas, and made it much more likely that we will react with extreme prejudice and nastiness to these ideas.

The first of these is A Very Good Thing and the second of these is a Very Bad Thing. But we would do well to hold on to the first, rather than abandon both.

How? Ah, now if only we had some extreme ideas about that.

Futurology from 1967

Did no work of science fiction/futurology anticipate miniaturization? Genuine question.

The meta-epistemology of the rate hike

Soon after I started blogging, Tyler Cowen joked, “You’re not really a blogger.” His point: Unlike most of the competition, I wasn’t reacting to the latest news or whatever’s hot. My goal as a blogger has always been to write think-pieces that stand the test of time.

https://www.econlib.org/a-fond-farewell-to-econlog/

I don’t know about standing the test of time where posts on EFE are concerned, but my approach to blogging is very similar: I prefer to not write about events immediately after they’ve occurred. This for a variety of reasons, not least of which is the fact that I’m lazy, and reading a lot of stuff at very short notice is something I would rather not do.

Another reason is that the very best pieces on any event usually take time to bubble up in my feed, and waiting therefore makes sense.

By the way, if you aren’t yet subscribed to Bryan Caplan’s new blog, please do!


But that being said, let’s talk about yesterday’s rate hike.

One of the pieces that I enjoyed writing last year was on the concept of meta-epistemology, after reading a post about it by Zeynep Tufekci.

I’m going to post a screenshot rather than an extract, because the formatting of the post helps:

https://econforeverybody.com/2021/02/05/zeynep-tufekci-on-metaepistomology/

Honest question: does this apply to the Reserve Bank of India as well?

Is it the case that the cost of downplaying inflation as a major problem now exceed the benefits of doing so? Have the incentives flipped for the RBI? If so, on what basis? Is there a sense, based on preliminary data, that inflation is a problem that can no longer be ignored?

And if so, how should we be interpreting not just the fact that rates have been raised, but the manner and the timing of the raise? In other words, are there two messages being sent out by the RBI: the message itself, and the implicit message encoded in the timing of the message?

And have (or will) the markets internalize this message, and if yes, what is to follow?


Learning about inflation, monetary policy, and the efficient market hypothesis via textbooks is less than half of the story. Take your view/model of how the world works to the world itself, and update your model as the years roll by.

Fun, exhilarating and occasionally nerve-wracking.

But it is the best way to learn.

Happy Birthday to Kevin Kelly

70th birthday that too!

Who is Kevin Kelly, you ask? Lots of ways to begin, but my favorite learning from Kevin Kelly (so far) has been the idea of 1000 true fans:

To be a successful creator you don’t need millions. You don’t need millions of dollars or millions of customers, millions of clients or millions of fans. To make a living as a craftsperson, photographer, musician, designer, author, animator, app maker, entrepreneur, or inventor you need only thousands of true fans.
A true fan is defined as a fan that will buy anything you produce. These diehard fans will drive 200 miles to see you sing; they will buy the hardback and paperback and audible versions of your book; they will purchase your next figurine sight unseen; they will pay for the “best-of” DVD version of your free youtube channel; they will come to your chef’s table once a month. If you have roughly a thousand of true fans like this (also known as super fans), you can make a living — if you are content to make a living but not a fortune.

https://kk.org/thetechnium/1000-true-fans/

I cannot for the life of me remember where I read about 1000 true fans first, but it most likely was via Tim Ferriss. (As an aside, Kevin Kelly has advice about this as well!) The extract above is an assertion, and if your reaction is along the lines of “but why is this assertion true?” – and I hope that is the case! – you will want to read the rest of the essay. It’s got spin-offs too, this essay, which only drives up my opinion of the original.

But Kevin Kelly is a person who you should spend time learning more about. Start with his Wikipedia page, listen to his multiple episodes with Russ Roberts over on EconTalk, visit the Cool Tools section on his website, subscribe to his related newsletter, listen to his podcasts with Tim Ferriss, and as a bonus, listen to Tyler Cowen’s podcast with Stewart Brand. And read his books, of course.

Long story short, he is a person worth knowing about, and trust me when I say we’ve only scratched the surface, if that. But today, I wanted to point you to his birthday gift to all of us, a lovely set of 103 observations that he has called “103 Bits of Advice I Wish I Had Known“. It goes without saying that all 103 are worth a ponder, but I’ll list here ten that especially resonated with me right now:

  1. About 99% of the time, the right time is right now.
  2. Anything you say before the word “but” does not count.
  3. When you forgive others, they may not notice, but you will heal. Forgiveness is not something we do for others; it is a gift to ourselves.
  4. When you lead, your real job is to create more leaders, not more followers.
  5. It is the duty of a student to get everything out of a teacher, and the duty of a teacher to get everything out of a student.
  6. Productivity is often a distraction. Don’t aim for better ways to get through your tasks as quickly as possible, rather aim for better tasks that you never want to stop doing.
  7. The consistency of your endeavors (exercise, companionship, work) is more important than the quantity. Nothing beats small things done every day, which is way more important than what you do occasionally.
  8. Half the skill of being educated is learning what you can ignore.
  9. When you have some success, the feeling of being an imposter can be real. Who am I fooling? But when you create things that only you — with your unique talents and experience — can do, then you are absolutely not an imposter. You are the ordained. It is your duty to work on things that only you can do.
  10. Your best job will be one that you were unqualified for because it stretches you. In fact only apply to jobs you are unqualified for.
  11. It’s possible that a not-so smart person, who can communicate well, can do much better than a super smart person who can’t communicate well. That is good news because it is much easier to improve your communication skills than your intelligence.
  12. For the best results with your children, spend only half the money you think you should, but double the time with them.
  13. Don’t bother fighting the old; just build the new.
  14. You are as big as the things that make you angry.
  15. Efficiency is highly overrated; Goofing off is highly underrated. Regularly scheduled sabbaths, sabbaticals, vacations, breaks, aimless walks and time off are essential for top performance of any kind. The best work ethic requires a good rest ethic.

The observant among you might have noticed that I ended up picking fifteen rather than ten, but why short change myself and my readers? I didn’t bother culling out five – and to be clear, this is not to imply that the other eighty-eight are somehow inferior. These fifteen resonated the most with me, and I sincerely hope that your list is completely different from mine.

Note to self: of the ones I have selected here, the fifth one is the one where I really need to pull up my socks.

And speaking of hope, it would be nice if this list sparked conversations and your own lists!

Past mentions of Kevin Kelly on this blog are here.

AI/ML: Some Thoughts

This is a true story, but I’ll (of course) anonymize the name of the educational institute and the student concerned:

One of the semester end examinations conducted during the pandemic at an educational institute had an error. Students asked about the error, and since the professor who had designed the paper was not available, another professor was asked what could be done. Said professor copied the text of the question and searched for it online, in the hope that the question (or a variant thereof) had been sourced online.

Alas, that didn’t work, but a related discovery was made. A student writing that same question paper had copied the question, and put it up for folks online to solve. It hadn’t been solved yet, but the fact that all of this could happen so quickly was mind-boggling.

The kicker? The student in question had not bothered to remain anonymous. Their name had been appended with the question.

Welcome to learning and examinations in the time of Coviid-19.


I have often joked in my classes in this past decade that it is only a matter of time before professors outsource the design of the question paper to freelance websites online – and students outsource the writing of the submission online. And who knows, it may end up being the same freelancer doing both of these “projects”.

All of which is a very roundabout way to get to thinking about Elicit, videos about which I had put up yesterday.

But let’s begin at the beginning: what is Elicit?

Elicit is a GPT-3 powered research assistant. Elicit helps you classify datasets, brainstorm research questions, and search through publications.

https://www.google.com/search?q=what+is+elicit.org

Which of course begs a follow-up question: what is GPT-3? And if you haven’t discovered GPT-3 yet, well, buckle up for the ride:

GPT-3 belongs to a category of deep learning known as a large language model, a complex neural net that has been trained on a titanic data set of text: in GPT-3’s case, roughly 700 gigabytes of data drawn from across the web, including Wikipedia, supplemented with a large collection of text from digitized books. GPT-3 is the most celebrated of the large language models, and the most publicly available, but Google, Meta (formerly known as Facebook) and DeepMind have all developed their own L.L.M.s in recent years. Advances in computational power — and new mathematical techniques — have enabled L.L.M.s of GPT-3’s vintage to ingest far larger data sets than their predecessors, and employ much deeper layers of artificial neurons for their training.
Chances are you have already interacted with a large language model if you’ve ever used an application — like Gmail — that includes an autocomplete feature, gently prompting you with the word ‘‘attend’’ after you type the sentence ‘‘Sadly I won’t be able to….’’ But autocomplete is only the most rudimentary expression of what software like GPT-3 is capable of. It turns out that with enough training data and sufficiently deep neural nets, large language models can display remarkable skill if you ask them not just to fill in the missing word, but also to continue on writing whole paragraphs in the style of the initial prompt.

https://www.nytimes.com/2022/04/15/magazine/ai-language.html

It’s wild, there’s no other way to put it:


So, OK, cool tech. But cool tech without the ability to apply it is less than half of the story. So what might be some applications of GPT-3?

A few months after GPT-3 went online, the OpenAI team discovered that the neural net had developed surprisingly effective skills at writing computer software, even though the training data had not deliberately included examples of code. It turned out that the web is filled with countless pages that include examples of computer programming, accompanied by descriptions of what the code is designed to do; from those elemental clues, GPT-3 effectively taught itself how to program. (OpenAI refined those embryonic coding skills with more targeted training, and now offers an interface called Codex that generates structured code in a dozen programming languages in response to natural-language instructions.)

https://www.nytimes.com/2022/04/15/magazine/ai-language.html

For example:

(Before we proceed, assuming it is not behind a paywall, please read the entire article from the NYT.)


But about a week ago or so, I first heard about Elicit.org:

Watch the video, play around with the tool once you register (it’s free) and if you are at all involved with academia, reflect on how much has changed, and how much more is likely to change in the time to come.

But there are things to worry about, of course. An excellent place to begin is with this essay by Emily M. Blender, on Medium. It’s a great essay, and deserves to be read in full. Here’s one relevant extract:

There is a talk I’ve given a couple of times now (first at the University of Edinburgh in August 2021) titled “Meaning making with artificial interlocutors and risks of language technology”. I end that talk by reminding the audience to not be too impressed, and to remember:
Just because that text seems coherent doesn’t mean the model behind it has understood anything or is trustworthy
Just because that answer was correct doesn’t mean the next one will be
When a computer seems to “speak our language”, we’re actually the ones doing all of the work

https://medium.com/@emilymenonbender/on-nyt-magazine-on-ai-resist-the-urge-to-be-impressed-3d92fd9a0edd

I haven’t seen the talk at the University of Edinburgh referred to in the extract, but it’s on my to-watch list. Here is the link, if you’re interested.

And here’s a Twitter thread by Emily M. Blender about Elicit.org specifically:


In response to this critique and other feedback, Elicit.org have come up with an explainer of sorts about how to use Elicit.org responsibly:

https://ought.org/updates/2022-04-25-responsibility

Before we proceed, I hope aficionados of statistics have noted the null hypothesis problem (which error would you rather avoid) in the last sentence of pt. 1 in that clipping above!


So all that being said, what do I think about GPT3 in general and elicit.org in particular?

I’m a sucker for trying out new things, especially from the world of tech. Innocent until proven guilty is a good maxim for approaching many things in life, and to me, so also with new tech. I’m gobsmacked to see tools like GPT3 and DallE2, and their applications to new tasks is amazing to see.

But that being said, there is a lot to think about, be wary of and guard against. I’m happy to keep an open mind and try these amazing technologies out, while keeping a close eye on what thoughtful critics have to say.

Which is exactly what I plan to do!

And for a person with a plan such as mine, what a time to be alive, no?

Have you tried Elicit.org yet?

Video 1:

And Video 2: